Image segmentation of the ventricular septum in fetal cardiac ultrasound videos based on deep learning using time-series information

Ai Dozen, Masaaki Komatsu, Akira Sakai, Reina Komatsu, Kanto Shozu, Hidenori Machino, Suguru Yasutomi, Tatsuya Arakaki, Ken Asada, Syuzo Kaneko, Ryu Matsuoka, Daisuke Aoki, Akihiko Sekizawa, Ryuji Hamamoto

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC.

Original languageEnglish
Article number1526
Pages (from-to)1-17
Number of pages17
JournalBiomolecules
Volume10
Issue number11
DOIs
Publication statusPublished - 2020 Nov

Keywords

  • Congenital heart disease
  • Deep learning
  • Fetal cardiac ultrasound video
  • Segmentation
  • Ventricular septum

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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